ENAS
E899017
ENAS (Efficient Neural Architecture Search) is a method that dramatically reduces the computational cost of neural architecture search by sharing parameters among many candidate architectures within a single super-network.
All labels observed (1)
| Label | Occurrences |
|---|---|
| ENAS canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T11003096 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ENAS Context triple: [Neural Architecture Search, notableMethod, ENAS]
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A.
ENBA
ENBA is the commonly used abbreviation for the Escola Nacional de Belas Artes, a prominent national fine arts school.
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B.
ENA
ENA is a prestigious French grande école that trained many of the country’s top civil servants and political leaders.
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C.
ENEA
ENEA is Italy’s national agency for new technologies, energy, and sustainable economic development, focused on research and innovation in fields such as energy, environment, and advanced technologies.
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D.
ENK
ENK is the ICAO airline designator assigned to Equair, an Ecuadorian commercial airline.
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E.
AANES
AANES is a de facto self-governing political entity in northern and eastern Syria, often associated with Kurdish-led, multi-ethnic autonomous administration and its experiment in decentralized, democratic governance.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: ENAS Target entity description: ENAS (Efficient Neural Architecture Search) is a method that dramatically reduces the computational cost of neural architecture search by sharing parameters among many candidate architectures within a single super-network.
-
A.
ENBA
ENBA is the commonly used abbreviation for the Escola Nacional de Belas Artes, a prominent national fine arts school.
-
B.
ENA
ENA is a prestigious French grande école that trained many of the country’s top civil servants and political leaders.
-
C.
ENEA
ENEA is Italy’s national agency for new technologies, energy, and sustainable economic development, focused on research and innovation in fields such as energy, environment, and advanced technologies.
-
D.
ENK
ENK is the ICAO airline designator assigned to Equair, an Ecuadorian commercial airline.
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E.
AANES
AANES is a de facto self-governing political entity in northern and eastern Syria, often associated with Kurdish-led, multi-ethnic autonomous administration and its experiment in decentralized, democratic governance.
- F. None of above. chosen
Statements (47)
| Predicate | Object |
|---|---|
| instanceOf |
AutoML technique
ⓘ
neural architecture search method ⓘ |
| abbreviationFor | Efficient Neural Architecture Search NERFINISHED ⓘ |
| aimsTo | reduce computational cost of neural architecture search ⓘ |
| appliedTo |
image classification
ⓘ
language modeling ⓘ |
| assumes | weight sharing does not overly bias architecture evaluation ⓘ |
| benchmarkDataset |
CIFAR-10
NERFINISHED
ⓘ
Penn Treebank NERFINISHED ⓘ |
| category |
meta-learning method
ⓘ
model search algorithm ⓘ |
| citationType | highly cited NAS paper ⓘ |
| comparedTo |
NASNet
NERFINISHED
ⓘ
Neural Architecture Search with Reinforcement Learning NERFINISHED ⓘ |
| controllerOutput | architecture decisions ⓘ |
| domain | deep learning ⓘ |
| evaluationMetric | validation performance of sampled architectures ⓘ |
| field |
artificial intelligence
ⓘ
machine learning ⓘ |
| fullName | Efficient Neural Architecture Search NERFINISHED ⓘ |
| hasTitle | Efficient Neural Architecture Search via Parameter Sharing NERFINISHED ⓘ |
| improvesOver | standard neural architecture search in efficiency ⓘ |
| influenced | later efficient NAS methods ⓘ |
| optimizesFor |
computational efficiency
ⓘ
validation accuracy ⓘ |
| organizationAffiliation | Google Brain NERFINISHED ⓘ |
| proposedBy |
Barret Zoph
NERFINISHED
ⓘ
Hieu Pham NERFINISHED ⓘ Jeff Dean NERFINISHED ⓘ Melody Y. Guan NERFINISHED ⓘ Quoc V. Le NERFINISHED ⓘ |
| publicationYear | 2018 ⓘ |
| publishedIn | arXiv NERFINISHED ⓘ |
| reduces |
GPU hours required for architecture search
ⓘ
search time by orders of magnitude compared to earlier NAS methods ⓘ |
| samples | subgraphs from a super-network ⓘ |
| searchesOver | neural network architectures ⓘ |
| searchGranularity | cell-level architecture search ⓘ |
| searchSpaceType | cell-based search space ⓘ |
| searchStrategy | RL-based controller over shared-weights supernet ⓘ |
| sharesParametersAmong | candidate architectures ⓘ |
| superNetworkType | directed acyclic graph ⓘ |
| trains | a single super-network ⓘ |
| uses |
controller RNN
ⓘ
parameter sharing ⓘ reinforcement learning ⓘ |
| usesOptimization | policy gradient ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: ENAS Description of subject: ENAS (Efficient Neural Architecture Search) is a method that dramatically reduces the computational cost of neural architecture search by sharing parameters among many candidate architectures within a single super-network.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.